Role & Focus
Our AI Engineers operate at the intersection of data engineering, software development and applied artificial intelligence.
Their focus is not on isolated experiments or proof-of-concept demonstrations, but on building AI solutions that function reliably within existing business processes, applications and data platforms.
The role focuses on:
- Developing AI and machine learning solutions for real-world business challenges
- Integrating AI models into existing applications and data platforms
- Designing scalable AI workflows and automation solutions
- Improving the reliability, performance and maintainability of AI systems
- Translating business processes into practical AI applications
- Supporting organisations in moving from proof of concept to production deployment
Where This Role Adds Value
This role becomes particularly relevant when AI is no longer an innovation project, but needs to become an integrated part of day-to-day operations and decision-making.
Typical situations include:
- AI prototypes show promise but are not yet production-ready
- Models produce inconsistent or difficult-to-explain outcomes
- Internal teams lack the expertise required to implement AI at scale
- AI solutions operate separately from existing systems, applications and data platforms
- There is a growing need for governance, monitoring and operational reliability
- Organisations want to leverage AI in a practical way without introducing unnecessary complexity
What to Expect from Full Orbit
Our AI Engineers:
- Understand both data infrastructure and AI models
- Operate independently within complex technical environments
- Combine experimentation with structured implementation
- Take a pragmatic, business-oriented approach
- Deliver solutions that remain scalable, maintainable and manageable
- Work closely with data engineers, software development teams and business stakeholders
We do not provide generic AI resources. Instead, we carefully match experienced specialists to your technical environment, business objectives and level of AI maturity.
Technology Landscape
Our AI Engineers work within modern AI and data ecosystems, including:
- AI & Machine Learning Frameworks: OpenAI, LangChain, Hugging Face, TensorFlow, PyTorch
- Data platforms: Databricks, Snowflake, Microsoft Fabric
- Cloud platforms: Azure, AWS, OCI
- Programming Languages: Python, SQL
- Orchestration & Data Pipelines: Airflow, dbt
- Vector Databases & AI Tooling: Pinecone, ChromaDB, Weaviate
Technology is never the objective in itself. It is applied to deliver reliable, scalable and practical AI solutions that create measurable business value.
Example in Practice
At a large mental healthcare organisation, Full Orbit helped make treatment goals more concrete, measurable and actionable through the use of AI.
The challenge was not purely technological. The real objective was to translate often abstract support requests into a structured and practical approach that could be embedded within the existing treatment process. By integrating AI into the organisation’s established way of working, a goal-generation solution was developed that helps practitioners create clear, SMART-formulated treatment goals more quickly and consistently.
The result was a reduction in administrative effort, more time for meaningful patient interactions, improved progress monitoring and better support for clients throughout their treatment journey.
How We Work
- A clear intake focused on the role, technical context and AI maturity of your organisation
- Selection based on both technical depth and practical implementation capabilities
- A focus on engineers who can connect AI with existing systems, data and business processes
- Flexible engagement models aligned with your situation, objectives and roadmap
Our goal is simple: to place the right specialist in the right role within a short timeframe.
Looking for an AI Engineer Who Goes Beyond Experimentation?
We ensure you quickly gain access to a senior professional who delivers AI solutions that create real and measurable value within your organisation.
